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--- |
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license: mit |
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base_model: JackFram/llama-68m |
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tags: |
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- tiny-model |
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- random-weights |
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- testing |
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- llama |
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--- |
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# Llama-3.3-Tiny-Instruct |
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This is a tiny random version of the JackFram/llama-68m model, created for testing and experimentation purposes. |
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## Model Details |
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- **Base model**: JackFram/llama-68m |
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- **Seed**: 42 |
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- **Hidden size**: 768 |
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- **Number of layers**: 2 |
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- **Number of attention heads**: 12 |
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- **Vocabulary size**: 32000 |
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- **Max position embeddings**: 2048 |
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## Parameters |
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- **Total parameters**: ~43,454,976 |
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- **Trainable parameters**: ~43,454,976 |
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## Usage |
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```python |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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# Load model and tokenizer |
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model = AutoModelForSequenceClassification.from_pretrained("AlignmentResearch/Llama-3.3-Tiny-Classifier") |
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tokenizer = AutoTokenizer.from_pretrained("AlignmentResearch/Llama-3.3-Tiny-Classifier") |
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# Generate text (note: this model has random weights!) |
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inputs = tokenizer("Hello, how are you?", return_tensors="pt") |
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outputs = model.generate(**inputs, max_length=50) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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## Important Notes |
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⚠️ **This model has random weights and is not trained!** It's designed for: |
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- Testing model loading and inference pipelines |
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- Benchmarking model architecture |
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- Educational purposes |
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- Rapid prototyping where actual model performance isn't needed |
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The model will generate random/nonsensical text since it hasn't been trained on any data. |
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## Creation |
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This model was created using the `upload_tiny_llama33.py` script from the minimal-grpo-trainer repository. |
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